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Clockwork – Intelligent, Composable Infrastructure Primitives in Python

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Clockwork – Intelligent, Composable Infrastructure Primitives in Python

I've been working on Clockwork, a Python library for composable infrastructure blocks that lets you dial the AI involvement up or down per resource. The core idea: Allows you to build complex infra components from basic building blocks with a knob on how much "intelligence" you want in it. ``` # Specify everything yourself nginx = DockerResource( image="nginx:1.25-alpine", ports=["8080:80"], volumes=["/configs:/etc/nginx"] ) # Just set constraints, AI fills the rest nginx = DockerResource( description="web server with caching", ports=["8080:80"] ) # Or just describe it nginx = DockerResource( description="web server for static files", assertions=[HealthcheckAssert(url="http://localhost:8080")] ) ``` Same resource type, you pick the level of control. What I find tedious (picking nginx vs caddy vs httpd) you might care deeply about. So every resource lets you specify what matters to you and skip what doesn't. It's built on Pulumi for deployment, uses Pydantic for declarative specifications, and works with local LLMs (LM Studio) and cloud-based such as OpenRouter. Also has composable resources - group related things together: ``` BlankResource(name="dev-stack", description="Local dev environment").add( DockerResource(description="postgres", ports=["5432:5432"]), DockerResource(description="redis", ports=["6379:6379"]), DockerResource(description="api server", ports=["8000:8000"]) ) ``` The AI sees the whole group and configures things to work together. Or you can .connect() independent resources for dependency ordering and auto-generated connection strings (this is still WIP as is the whole project but I'm currently thinking of a mechanism of "connecting" things together appropriately). Repo: https://github.com/kessler-frost/clockwork It's early (v0.3.0) and I'm still figuring out what works. Main questions: 1. The "adjustable AI" concept - is this useful or confusing? 2. Which resources/features would be most valuable next? Would love to hear if this resonates with anyone or if I'm solving a problem nobody has.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: dock, using, open · Missing: mac, agents, macos
75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, nginx, ide · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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